Overview

Dataset statistics

Number of variables33
Number of observations5819079
Missing cells30465274
Missing cells (%)15.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 GiB
Average record size in memory264.0 B

Variable types

Categorical6
Numeric16
Text2
Unsupported2
DateTime7

Alerts

YEAR has constant value ""Constant
DEPARTURE_DELAY is highly overall correlated with ARRIVAL_DELAYHigh correlation
ELAPSED_TIME is highly overall correlated with AIR_TIME and 3 other fieldsHigh correlation
AIR_TIME is highly overall correlated with ELAPSED_TIME and 3 other fieldsHigh correlation
DISTANCE is highly overall correlated with ELAPSED_TIME and 1 other fieldsHigh correlation
TAXI_IN is highly overall correlated with CANCELLEDHigh correlation
ARRIVAL_DELAY is highly overall correlated with DEPARTURE_DELAY and 2 other fieldsHigh correlation
AIR_SYSTEM_DELAY is highly overall correlated with DIVERTED and 1 other fieldsHigh correlation
SECURITY_DELAY is highly overall correlated with DIVERTED and 1 other fieldsHigh correlation
AIRLINE_DELAY is highly overall correlated with DIVERTED and 1 other fieldsHigh correlation
LATE_AIRCRAFT_DELAY is highly overall correlated with DIVERTED and 1 other fieldsHigh correlation
WEATHER_DELAY is highly overall correlated with DIVERTED and 1 other fieldsHigh correlation
DIVERTED is highly overall correlated with ELAPSED_TIME and 8 other fieldsHigh correlation
CANCELLED is highly overall correlated with ELAPSED_TIME and 9 other fieldsHigh correlation
CANCELLATION_REASON is highly overall correlated with DIVERTED and 2 other fieldsHigh correlation
LATE_AIRCRAFT_DELAY_CAT is highly overall correlated with CANCELLATION_REASONHigh correlation
DIVERTED is highly imbalanced (97.4%)Imbalance
CANCELLED is highly imbalanced (88.5%)Imbalance
LATE_AIRCRAFT_DELAY_CAT is highly imbalanced (76.3%)Imbalance
DEPARTURE_TIME has 86153 (1.5%) missing valuesMissing
DEPARTURE_DELAY has 86153 (1.5%) missing valuesMissing
TAXI_OUT has 89047 (1.5%) missing valuesMissing
WHEELS_OFF has 89047 (1.5%) missing valuesMissing
ELAPSED_TIME has 105071 (1.8%) missing valuesMissing
AIR_TIME has 105071 (1.8%) missing valuesMissing
WHEELS_ON has 92513 (1.6%) missing valuesMissing
TAXI_IN has 92513 (1.6%) missing valuesMissing
ARRIVAL_TIME has 92513 (1.6%) missing valuesMissing
ARRIVAL_DELAY has 105071 (1.8%) missing valuesMissing
CANCELLATION_REASON has 5729195 (98.5%) missing valuesMissing
AIR_SYSTEM_DELAY has 4755640 (81.7%) missing valuesMissing
SECURITY_DELAY has 4755640 (81.7%) missing valuesMissing
AIRLINE_DELAY has 4755640 (81.7%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 4755640 (81.7%) missing valuesMissing
WEATHER_DELAY has 4755640 (81.7%) missing valuesMissing
SECURITY_DELAY is highly skewed (γ1 = 72.12766122)Skewed
ORIGIN_AIRPORT is an unsupported type, check if it needs cleaning or further analysisUnsupported
DESTINATION_AIRPORT is an unsupported type, check if it needs cleaning or further analysisUnsupported
DEPARTURE_DELAY has 329360 (5.7%) zerosZeros
ARRIVAL_DELAY has 126213 (2.2%) zerosZeros
AIR_SYSTEM_DELAY has 498613 (8.6%) zerosZeros
SECURITY_DELAY has 1059955 (18.2%) zerosZeros
AIRLINE_DELAY has 493417 (8.5%) zerosZeros
LATE_AIRCRAFT_DELAY has 506486 (8.7%) zerosZeros
WEATHER_DELAY has 998723 (17.2%) zerosZeros

Reproduction

Analysis started2024-02-17 17:37:32.097163
Analysis finished2024-02-17 17:42:38.121933
Duration5 minutes and 6.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

YEAR
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.4 MiB
2015
5819079 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters23276316
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 5819079
100.0%

Length

2024-02-18T01:42:38.159443image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-18T01:42:38.413629image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
2015 5819079
100.0%

Most occurring characters

ValueCountFrequency (%)
2 5819079
25.0%
0 5819079
25.0%
1 5819079
25.0%
5 5819079
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23276316
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5819079
25.0%
0 5819079
25.0%
1 5819079
25.0%
5 5819079
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23276316
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5819079
25.0%
0 5819079
25.0%
1 5819079
25.0%
5 5819079
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23276316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5819079
25.0%
0 5819079
25.0%
1 5819079
25.0%
5 5819079
25.0%

MONTH
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5240852
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:38.456631image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4051368
Coefficient of variation (CV)0.52193323
Kurtosis-1.1756822
Mean6.5240852
Median Absolute Deviation (MAD)3
Skewness-0.0036838264
Sum37964167
Variance11.594957
MonotonicityIncreasing
2024-02-18T01:42:38.505690image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 520718
8.9%
8 510536
8.8%
3 504312
8.7%
6 503897
8.7%
5 496993
8.5%
10 486165
8.4%
4 485151
8.3%
12 479230
8.2%
1 469968
8.1%
11 467972
8.0%
Other values (2) 894137
15.4%
ValueCountFrequency (%)
1 469968
8.1%
2 429191
7.4%
3 504312
8.7%
4 485151
8.3%
5 496993
8.5%
6 503897
8.7%
7 520718
8.9%
8 510536
8.8%
9 464946
8.0%
10 486165
8.4%
ValueCountFrequency (%)
12 479230
8.2%
11 467972
8.0%
10 486165
8.4%
9 464946
8.0%
8 510536
8.8%
7 520718
8.9%
6 503897
8.7%
5 496993
8.5%
4 485151
8.3%
3 504312
8.7%

DAY
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.704594
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:38.568199image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7834251
Coefficient of variation (CV)0.55929017
Kurtosis-1.1892082
Mean15.704594
Median Absolute Deviation (MAD)8
Skewness0.0086667031
Sum91386273
Variance77.148556
MonotonicityNot monotonic
2024-02-18T01:42:38.622200image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 195986
 
3.4%
16 195899
 
3.4%
20 195707
 
3.4%
13 195089
 
3.4%
9 194224
 
3.3%
8 193964
 
3.3%
23 193560
 
3.3%
19 193284
 
3.3%
15 192950
 
3.3%
22 192725
 
3.3%
Other values (21) 3875691
66.6%
ValueCountFrequency (%)
1 189477
3.3%
2 195986
3.4%
3 190007
3.3%
4 190893
3.3%
5 189766
3.3%
6 191232
3.3%
7 187598
3.2%
8 193964
3.3%
9 194224
3.3%
10 189288
3.3%
ValueCountFrequency (%)
31 103812
1.8%
30 178771
3.1%
29 179441
3.1%
28 191401
3.3%
27 191920
3.3%
26 187387
3.2%
25 187317
3.2%
24 185017
3.2%
23 193560
3.3%
22 192725
3.3%

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9269412
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:38.673706image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.988845
Coefficient of variation (CV)0.50646162
Kurtosis-1.2117267
Mean3.9269412
Median Absolute Deviation (MAD)2
Skewness0.057035635
Sum22851181
Variance3.9555045
MonotonicityNot monotonic
2024-02-18T01:42:38.714706image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 872521
15.0%
1 865543
14.9%
5 862209
14.8%
3 855897
14.7%
2 844600
14.5%
7 817764
14.1%
6 700545
12.0%
ValueCountFrequency (%)
1 865543
14.9%
2 844600
14.5%
3 855897
14.7%
4 872521
15.0%
5 862209
14.8%
6 700545
12.0%
7 817764
14.1%
ValueCountFrequency (%)
7 817764
14.1%
6 700545
12.0%
5 862209
14.8%
4 872521
15.0%
3 855897
14.7%
2 844600
14.5%
1 865543
14.9%

AIRLINE
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.4 MiB
WN
1261855 
DL
875881 
AA
725984 
OO
588353 
EV
571977 
Other values (9)
1795029 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters11638158
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAS
2nd rowAA
3rd rowUS
4th rowAA
5th rowAS

Common Values

ValueCountFrequency (%)
WN 1261855
21.7%
DL 875881
15.1%
AA 725984
12.5%
OO 588353
10.1%
EV 571977
9.8%
UA 515723
8.9%
MQ 294632
 
5.1%
B6 267048
 
4.6%
US 198715
 
3.4%
AS 172521
 
3.0%
Other values (4) 346390
 
6.0%

Length

2024-02-18T01:42:38.768214image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 1261855
21.7%
dl 875881
15.1%
aa 725984
12.5%
oo 588353
10.1%
ev 571977
9.8%
ua 515723
8.9%
mq 294632
 
5.1%
b6 267048
 
4.6%
us 198715
 
3.4%
as 172521
 
3.0%
Other values (4) 346390
 
6.0%

Most occurring characters

ValueCountFrequency (%)
A 2216484
19.0%
N 1379234
11.9%
W 1261855
10.8%
O 1176706
10.1%
D 875881
 
7.5%
L 875881
 
7.5%
U 714438
 
6.1%
V 633880
 
5.4%
E 571977
 
4.9%
S 371236
 
3.2%
Other values (9) 1560586
13.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11280274
96.9%
Decimal Number 357884
 
3.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2216484
19.6%
N 1379234
12.2%
W 1261855
11.2%
O 1176706
10.4%
D 875881
 
7.8%
L 875881
 
7.8%
U 714438
 
6.3%
V 633880
 
5.6%
E 571977
 
5.1%
S 371236
 
3.3%
Other values (7) 1202702
10.7%
Decimal Number
ValueCountFrequency (%)
6 267048
74.6%
9 90836
 
25.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 11280274
96.9%
Common 357884
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2216484
19.6%
N 1379234
12.2%
W 1261855
11.2%
O 1176706
10.4%
D 875881
 
7.8%
L 875881
 
7.8%
U 714438
 
6.3%
V 633880
 
5.6%
E 571977
 
5.1%
S 371236
 
3.3%
Other values (7) 1202702
10.7%
Common
ValueCountFrequency (%)
6 267048
74.6%
9 90836
 
25.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11638158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2216484
19.0%
N 1379234
11.9%
W 1261855
10.8%
O 1176706
10.1%
D 875881
 
7.5%
L 875881
 
7.5%
U 714438
 
6.1%
V 633880
 
5.4%
E 571977
 
4.9%
S 371236
 
3.2%
Other values (9) 1560586
13.4%

FLIGHT_NUMBER
Real number (ℝ)

Distinct6952
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2173.0927
Minimum1
Maximum9855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:38.827214image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile167
Q1730
median1690
Q33230
95-th percentile5565
Maximum9855
Range9854
Interquartile range (IQR)2500

Descriptive statistics

Standard deviation1757.064
Coefficient of variation (CV)0.80855454
Kurtosis-0.27916561
Mean2173.0927
Median Absolute Deviation (MAD)1096
Skewness0.85646125
Sum1.2645398 × 1010
Variance3087273.9
MonotonicityNot monotonic
2024-02-18T01:42:38.897721image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
469 3975
 
0.1%
327 3554
 
0.1%
326 3513
 
0.1%
188 3386
 
0.1%
403 3370
 
0.1%
667 3360
 
0.1%
407 3324
 
0.1%
315 3321
 
0.1%
223 3291
 
0.1%
61 3266
 
0.1%
Other values (6942) 5784719
99.4%
ValueCountFrequency (%)
1 2393
< 0.1%
2 1973
< 0.1%
3 2890
< 0.1%
4 1772
< 0.1%
5 2271
< 0.1%
6 1418
< 0.1%
7 2015
< 0.1%
8 2820
< 0.1%
9 1647
< 0.1%
10 1506
< 0.1%
ValueCountFrequency (%)
9855 1
 
< 0.1%
9794 1
 
< 0.1%
9793 1
 
< 0.1%
9320 1
 
< 0.1%
8445 1
 
< 0.1%
8442 1
 
< 0.1%
8410 1
 
< 0.1%
8409 1
 
< 0.1%
7438 516
< 0.1%
7433 3
 
< 0.1%
Distinct4897
Distinct (%)0.1%
Missing14721
Missing (%)0.3%
Memory size44.4 MiB
2024-02-18T01:42:39.027229image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.997398
Min length5

Characters and Unicode

Total characters34811045
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowN407AS
2nd rowN3KUAA
3rd rowN171US
4th rowN3HYAA
5th rowN527AS
ValueCountFrequency (%)
n480ha 3768
 
0.1%
n488ha 3723
 
0.1%
n484ha 3723
 
0.1%
n493ha 3585
 
0.1%
n478ha 3577
 
0.1%
n483ha 3528
 
0.1%
n486ha 3513
 
0.1%
n491ha 3494
 
0.1%
n489ha 3477
 
0.1%
n477ha 3402
 
0.1%
Other values (4887) 5768568
99.4%
2024-02-18T01:42:39.217745image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 6830260
19.6%
A 2241035
 
6.4%
3 2071106
 
5.9%
9 2001378
 
5.7%
6 1988220
 
5.7%
7 1961545
 
5.6%
1 1882399
 
5.4%
5 1860773
 
5.3%
4 1842975
 
5.3%
2 1762716
 
5.1%
Other values (24) 10368638
29.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18285372
52.5%
Uppercase Letter 16525673
47.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 6830260
41.3%
A 2241035
 
13.6%
W 1556927
 
9.4%
S 1214594
 
7.3%
U 549084
 
3.3%
D 526467
 
3.2%
B 460822
 
2.8%
Q 349218
 
2.1%
M 334524
 
2.0%
K 330402
 
2.0%
Other values (14) 2132340
 
12.9%
Decimal Number
ValueCountFrequency (%)
3 2071106
11.3%
9 2001378
10.9%
6 1988220
10.9%
7 1961545
10.7%
1 1882399
10.3%
5 1860773
10.2%
4 1842975
10.1%
2 1762716
9.6%
8 1725865
9.4%
0 1188395
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 18285372
52.5%
Latin 16525673
47.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 6830260
41.3%
A 2241035
 
13.6%
W 1556927
 
9.4%
S 1214594
 
7.3%
U 549084
 
3.3%
D 526467
 
3.2%
B 460822
 
2.8%
Q 349218
 
2.1%
M 334524
 
2.0%
K 330402
 
2.0%
Other values (14) 2132340
 
12.9%
Common
ValueCountFrequency (%)
3 2071106
11.3%
9 2001378
10.9%
6 1988220
10.9%
7 1961545
10.7%
1 1882399
10.3%
5 1860773
10.2%
4 1842975
10.1%
2 1762716
9.6%
8 1725865
9.4%
0 1188395
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34811045
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 6830260
19.6%
A 2241035
 
6.4%
3 2071106
 
5.9%
9 2001378
 
5.7%
6 1988220
 
5.7%
7 1961545
 
5.6%
1 1882399
 
5.4%
5 1860773
 
5.3%
4 1842975
 
5.3%
2 1762716
 
5.1%
Other values (24) 10368638
29.8%

ORIGIN_AIRPORT
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size44.4 MiB

DESTINATION_AIRPORT
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size44.4 MiB
Distinct1262
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.4 MiB
Minimum2024-02-18 00:00:00
Maximum2024-02-18 23:59:00
2024-02-18T01:42:39.298759image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:42:39.364908image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DEPARTURE_TIME
Date

MISSING 

Distinct1381
Distinct (%)< 0.1%
Missing86153
Missing (%)1.5%
Memory size44.4 MiB
Minimum2024-02-18 00:00:00
Maximum2024-02-18 23:59:00
2024-02-18T01:42:39.432906image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:42:39.503414image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DEPARTURE_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1217
Distinct (%)< 0.1%
Missing86153
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean9.3701583
Minimum-82
Maximum1988
Zeros329360
Zeros (%)5.7%
Negative3277948
Negative (%)56.3%
Memory size44.4 MiB
2024-02-18T01:42:39.568921image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-82
5-th percentile-9
Q1-5
median-2
Q37
95-th percentile67
Maximum1988
Range2070
Interquartile range (IQR)12

Descriptive statistics

Standard deviation37.080942
Coefficient of variation (CV)3.9573443
Kurtosis123.00564
Mean9.3701583
Median Absolute Deviation (MAD)4
Skewness7.5928693
Sum53718424
Variance1374.9963
MonotonicityNot monotonic
2024-02-18T01:42:39.634921image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3 455407
 
7.8%
-4 444053
 
7.6%
-5 438844
 
7.5%
-2 435237
 
7.5%
-1 387475
 
6.7%
0 329360
 
5.7%
-6 324242
 
5.6%
-7 242933
 
4.2%
-8 173407
 
3.0%
1 160076
 
2.8%
Other values (1207) 2341892
40.2%
ValueCountFrequency (%)
-82 1
 
< 0.1%
-68 1
 
< 0.1%
-61 1
 
< 0.1%
-56 1
 
< 0.1%
-55 1
 
< 0.1%
-52 1
 
< 0.1%
-48 2
< 0.1%
-47 1
 
< 0.1%
-46 1
 
< 0.1%
-45 4
< 0.1%
ValueCountFrequency (%)
1988 1
< 0.1%
1878 1
< 0.1%
1670 1
< 0.1%
1649 1
< 0.1%
1631 1
< 0.1%
1625 1
< 0.1%
1609 1
< 0.1%
1604 1
< 0.1%
1589 1
< 0.1%
1587 1
< 0.1%

TAXI_OUT
Real number (ℝ)

MISSING 

Distinct184
Distinct (%)< 0.1%
Missing89047
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean16.071662
Minimum1
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:39.701966image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q111
median14
Q319
95-th percentile31
Maximum225
Range224
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.8955741
Coefficient of variation (CV)0.55349434
Kurtosis24.002893
Mean16.071662
Median Absolute Deviation (MAD)4
Skewness3.4671477
Sum92091139
Variance79.131238
MonotonicityNot monotonic
2024-02-18T01:42:39.766259image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 463189
 
8.0%
11 462159
 
7.9%
13 440243
 
7.6%
10 430606
 
7.4%
14 402938
 
6.9%
9 360368
 
6.2%
15 359214
 
6.2%
16 312858
 
5.4%
17 270827
 
4.7%
8 261803
 
4.5%
Other values (174) 1965827
33.8%
ValueCountFrequency (%)
1 220
 
< 0.1%
2 353
 
< 0.1%
3 1716
 
< 0.1%
4 6141
 
0.1%
5 23185
 
0.4%
6 75226
 
1.3%
7 160802
 
2.8%
8 261803
4.5%
9 360368
6.2%
10 430606
7.4%
ValueCountFrequency (%)
225 1
 
< 0.1%
200 1
 
< 0.1%
185 1
 
< 0.1%
181 1
 
< 0.1%
180 1
 
< 0.1%
179 1
 
< 0.1%
178 1
 
< 0.1%
177 4
< 0.1%
176 1
 
< 0.1%
175 2
< 0.1%

WHEELS_OFF
Date

MISSING 

Distinct1381
Distinct (%)< 0.1%
Missing89047
Missing (%)1.5%
Memory size44.4 MiB
Minimum2024-02-18 00:00:00
Maximum2024-02-18 23:59:00
2024-02-18T01:42:39.834259image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:42:39.901320image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct550
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Memory size44.4 MiB
2024-02-18T01:42:40.024460image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters46552584
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row02:05 AM
2nd row02:80 AM
3rd row02:86 AM
4th row02:85 AM
5th row02:35 AM
ValueCountFrequency (%)
am 5819073
50.0%
00:85 115062
 
1.0%
00:80 112856
 
1.0%
00:75 105978
 
0.9%
00:90 101926
 
0.9%
00:70 96823
 
0.8%
00:65 91119
 
0.8%
00:95 85242
 
0.7%
01:10 79296
 
0.7%
01:15 74520
 
0.6%
Other values (541) 4956251
42.6%
2024-02-18T01:42:40.209836image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9589032
20.6%
: 5819073
12.5%
5819073
12.5%
A 5819073
12.5%
M 5819073
12.5%
1 3685602
 
7.9%
5 1775417
 
3.8%
2 1597071
 
3.4%
3 1202243
 
2.6%
7 1176189
 
2.5%
Other values (4) 4250738
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23276292
50.0%
Uppercase Letter 11638146
25.0%
Other Punctuation 5819073
 
12.5%
Space Separator 5819073
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9589032
41.2%
1 3685602
 
15.8%
5 1775417
 
7.6%
2 1597071
 
6.9%
3 1202243
 
5.2%
7 1176189
 
5.1%
8 1142696
 
4.9%
6 1124045
 
4.8%
9 1054456
 
4.5%
4 929541
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
A 5819073
50.0%
M 5819073
50.0%
Other Punctuation
ValueCountFrequency (%)
: 5819073
100.0%
Space Separator
ValueCountFrequency (%)
5819073
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34914438
75.0%
Latin 11638146
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9589032
27.5%
: 5819073
16.7%
5819073
16.7%
1 3685602
 
10.6%
5 1775417
 
5.1%
2 1597071
 
4.6%
3 1202243
 
3.4%
7 1176189
 
3.4%
8 1142696
 
3.3%
6 1124045
 
3.2%
Other values (2) 1983997
 
5.7%
Latin
ValueCountFrequency (%)
A 5819073
50.0%
M 5819073
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46552584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9589032
20.6%
: 5819073
12.5%
5819073
12.5%
A 5819073
12.5%
M 5819073
12.5%
1 3685602
 
7.9%
5 1775417
 
3.8%
2 1597071
 
3.4%
3 1202243
 
2.6%
7 1176189
 
2.5%
Other values (4) 4250738
9.1%

ELAPSED_TIME
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct712
Distinct (%)< 0.1%
Missing105071
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean137.00619
Minimum14
Maximum766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:40.291027image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile54
Q182
median118
Q3168
95-th percentile299
Maximum766
Range752
Interquartile range (IQR)86

Descriptive statistics

Standard deviation74.211072
Coefficient of variation (CV)0.54166218
Kurtosis2.0543165
Mean137.00619
Median Absolute Deviation (MAD)41
Skewness1.3532223
Sum7.8285446 × 108
Variance5507.2832
MonotonicityNot monotonic
2024-02-18T01:42:40.361533image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 47441
 
0.8%
79 47049
 
0.8%
81 46966
 
0.8%
82 46679
 
0.8%
78 46287
 
0.8%
77 46142
 
0.8%
76 46041
 
0.8%
83 45659
 
0.8%
84 45619
 
0.8%
75 45312
 
0.8%
Other values (702) 5250813
90.2%
(Missing) 105071
 
1.8%
ValueCountFrequency (%)
14 3
 
< 0.1%
15 9
 
< 0.1%
16 29
 
< 0.1%
17 46
< 0.1%
18 58
< 0.1%
19 56
< 0.1%
20 39
< 0.1%
21 70
< 0.1%
22 69
< 0.1%
23 80
< 0.1%
ValueCountFrequency (%)
766 1
< 0.1%
735 1
< 0.1%
733 1
< 0.1%
731 1
< 0.1%
730 1
< 0.1%
727 1
< 0.1%
726 1
< 0.1%
724 1
< 0.1%
721 1
< 0.1%
719 1
< 0.1%

AIR_TIME
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct675
Distinct (%)< 0.1%
Missing105071
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean113.51163
Minimum7
Maximum690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:40.437039image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile34
Q160
median94
Q3144
95-th percentile273
Maximum690
Range683
Interquartile range (IQR)84

Descriptive statistics

Standard deviation72.230822
Coefficient of variation (CV)0.63632971
Kurtosis2.0956915
Mean113.51163
Median Absolute Deviation (MAD)39
Skewness1.3783417
Sum6.4860635 × 108
Variance5217.2916
MonotonicityNot monotonic
2024-02-18T01:42:40.506073image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 49791
 
0.9%
63 49760
 
0.9%
62 49476
 
0.9%
65 49393
 
0.8%
61 49215
 
0.8%
43 48785
 
0.8%
60 48736
 
0.8%
59 48405
 
0.8%
66 48334
 
0.8%
44 48295
 
0.8%
Other values (665) 5223818
89.8%
(Missing) 105071
 
1.8%
ValueCountFrequency (%)
7 7
 
< 0.1%
8 68
 
< 0.1%
9 134
 
< 0.1%
10 128
 
< 0.1%
11 112
 
< 0.1%
12 88
 
< 0.1%
13 208
 
< 0.1%
14 549
 
< 0.1%
15 1118
< 0.1%
16 2007
< 0.1%
ValueCountFrequency (%)
690 2
< 0.1%
687 2
< 0.1%
684 1
< 0.1%
683 2
< 0.1%
682 1
< 0.1%
679 1
< 0.1%
678 1
< 0.1%
676 1
< 0.1%
674 1
< 0.1%
672 1
< 0.1%

DISTANCE
Real number (ℝ)

HIGH CORRELATION 

Distinct1363
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean822.35649
Minimum21
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:40.576265image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile173
Q1373
median647
Q31062
95-th percentile2227
Maximum4983
Range4962
Interquartile range (IQR)689

Descriptive statistics

Standard deviation607.78429
Coefficient of variation (CV)0.73907641
Kurtosis2.2473611
Mean822.35649
Median Absolute Deviation (MAD)322
Skewness1.4224682
Sum4.7853574 × 109
Variance369401.74
MonotonicityNot monotonic
2024-02-18T01:42:40.735337image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 50069
 
0.9%
447 28096
 
0.5%
594 27629
 
0.5%
404 27429
 
0.5%
2475 26219
 
0.5%
867 25496
 
0.4%
399 25118
 
0.4%
862 23799
 
0.4%
214 23454
 
0.4%
236 22316
 
0.4%
Other values (1353) 5539454
95.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
31 726
 
< 0.1%
36 1
 
< 0.1%
41 154
 
< 0.1%
49 1
 
< 0.1%
52 1
 
< 0.1%
62 2
 
< 0.1%
67 11177
0.2%
68 1524
 
< 0.1%
69 991
 
< 0.1%
ValueCountFrequency (%)
4983 682
< 0.1%
4962 722
< 0.1%
4817 344
 
< 0.1%
4502 729
< 0.1%
4243 730
< 0.1%
4184 238
 
< 0.1%
3972 94
 
< 0.1%
3904 730
< 0.1%
3801 730
< 0.1%
3784 1518
< 0.1%

WHEELS_ON
Date

MISSING 

Distinct1381
Distinct (%)< 0.1%
Missing92513
Missing (%)1.6%
Memory size44.4 MiB
Minimum2024-02-18 00:00:00
Maximum2024-02-18 23:59:00
2024-02-18T01:42:40.807395image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:42:40.875497image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TAXI_IN
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct185
Distinct (%)< 0.1%
Missing92513
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean7.4349708
Minimum1
Maximum248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:40.942497image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile16
Maximum248
Range247
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.6385477
Coefficient of variation (CV)0.75838195
Kurtosis58.922996
Mean7.4349708
Median Absolute Deviation (MAD)2
Skewness5.1297319
Sum42576851
Variance31.79322
MonotonicityNot monotonic
2024-02-18T01:42:41.012007image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 932909
16.0%
4 923558
15.9%
6 759134
13.0%
7 567620
9.8%
3 524797
9.0%
8 423947
7.3%
9 316600
 
5.4%
10 243087
 
4.2%
11 182533
 
3.1%
12 139532
 
2.4%
Other values (175) 712849
12.3%
ValueCountFrequency (%)
1 5695
 
0.1%
2 117453
 
2.0%
3 524797
9.0%
4 923558
15.9%
5 932909
16.0%
6 759134
13.0%
7 567620
9.8%
8 423947
7.3%
9 316600
 
5.4%
10 243087
 
4.2%
ValueCountFrequency (%)
248 1
< 0.1%
202 1
< 0.1%
197 1
< 0.1%
184 1
< 0.1%
183 1
< 0.1%
181 1
< 0.1%
180 1
< 0.1%
179 1
< 0.1%
178 1
< 0.1%
177 1
< 0.1%
Distinct1376
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.4 MiB
Minimum2024-02-18 00:00:00
Maximum2024-02-18 23:59:00
2024-02-18T01:42:41.083514image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:42:41.150514image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ARRIVAL_TIME
Date

MISSING 

Distinct1381
Distinct (%)< 0.1%
Missing92513
Missing (%)1.6%
Memory size44.4 MiB
Minimum2024-02-18 00:00:00
Maximum2024-02-18 23:59:00
2024-02-18T01:42:41.223563image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:42:41.292071image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ARRIVAL_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1240
Distinct (%)< 0.1%
Missing105071
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean4.4070574
Minimum-87
Maximum1971
Zeros126213
Zeros (%)2.2%
Negative3500899
Negative (%)60.2%
Memory size44.4 MiB
2024-02-18T01:42:41.359645image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-87
5-th percentile-25
Q1-13
median-5
Q38
95-th percentile66
Maximum1971
Range2058
Interquartile range (IQR)21

Descriptive statistics

Standard deviation39.271297
Coefficient of variation (CV)8.911002
Kurtosis97.739803
Mean4.4070574
Median Absolute Deviation (MAD)10
Skewness6.5028962
Sum25181961
Variance1542.2348
MonotonicityNot monotonic
2024-02-18T01:42:41.423081image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-8 176899
 
3.0%
-9 176016
 
3.0%
-10 175232
 
3.0%
-7 174524
 
3.0%
-11 171557
 
2.9%
-6 169411
 
2.9%
-12 165214
 
2.8%
-5 164176
 
2.8%
-13 158464
 
2.7%
-4 157472
 
2.7%
Other values (1230) 4025043
69.2%
ValueCountFrequency (%)
-87 2
< 0.1%
-82 1
 
< 0.1%
-81 2
< 0.1%
-80 3
< 0.1%
-79 2
< 0.1%
-78 1
 
< 0.1%
-77 2
< 0.1%
-76 3
< 0.1%
-75 1
 
< 0.1%
-74 3
< 0.1%
ValueCountFrequency (%)
1971 1
< 0.1%
1898 1
< 0.1%
1665 1
< 0.1%
1638 1
< 0.1%
1636 2
< 0.1%
1627 1
< 0.1%
1598 1
< 0.1%
1593 1
< 0.1%
1576 1
< 0.1%
1574 1
< 0.1%

DIVERTED
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.4 MiB
0
5803892 
1
 
15187

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5819079
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5803892
99.7%
1 15187
 
0.3%

Length

2024-02-18T01:42:41.484092image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-18T01:42:41.535093image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5803892
99.7%
1 15187
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 5803892
99.7%
1 15187
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5819079
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5803892
99.7%
1 15187
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5819079
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5803892
99.7%
1 15187
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5819079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5803892
99.7%
1 15187
 
0.3%

CANCELLED
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.4 MiB
0
5729195 
1
 
89884

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5819079
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5729195
98.5%
1 89884
 
1.5%

Length

2024-02-18T01:42:41.578599image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-18T01:42:41.631599image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5729195
98.5%
1 89884
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 5729195
98.5%
1 89884
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5819079
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5729195
98.5%
1 89884
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5819079
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5729195
98.5%
1 89884
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5819079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5729195
98.5%
1 89884
 
1.5%

CANCELLATION_REASON
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing5729195
Missing (%)98.5%
Memory size44.4 MiB
B
48851 
A
25262 
C
15749 
D
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89884
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
B 48851
 
0.8%
A 25262
 
0.4%
C 15749
 
0.3%
D 22
 
< 0.1%
(Missing) 5729195
98.5%

Length

2024-02-18T01:42:41.674107image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-18T01:42:41.734107image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
b 48851
54.3%
a 25262
28.1%
c 15749
 
17.5%
d 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
B 48851
54.3%
A 25262
28.1%
C 15749
 
17.5%
D 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 89884
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 48851
54.3%
A 25262
28.1%
C 15749
 
17.5%
D 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 89884
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 48851
54.3%
A 25262
28.1%
C 15749
 
17.5%
D 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 48851
54.3%
A 25262
28.1%
C 15749
 
17.5%
D 22
 
< 0.1%

AIR_SYSTEM_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct570
Distinct (%)0.1%
Missing4755640
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean13.480568
Minimum0
Maximum1134
Zeros498613
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:41.797614image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q318
95-th percentile56
Maximum1134
Range1134
Interquartile range (IQR)18

Descriptive statistics

Standard deviation28.003679
Coefficient of variation (CV)2.0773367
Kurtosis71.529141
Mean13.480568
Median Absolute Deviation (MAD)2
Skewness6.0267533
Sum14335762
Variance784.20603
MonotonicityNot monotonic
2024-02-18T01:42:41.864617image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 498613
 
8.6%
1 28003
 
0.5%
15 23199
 
0.4%
2 22981
 
0.4%
3 21446
 
0.4%
16 21357
 
0.4%
4 20305
 
0.3%
17 18738
 
0.3%
5 18737
 
0.3%
6 17671
 
0.3%
Other values (560) 372389
 
6.4%
(Missing) 4755640
81.7%
ValueCountFrequency (%)
0 498613
8.6%
1 28003
 
0.5%
2 22981
 
0.4%
3 21446
 
0.4%
4 20305
 
0.3%
5 18737
 
0.3%
6 17671
 
0.3%
7 16582
 
0.3%
8 15644
 
0.3%
9 14716
 
0.3%
ValueCountFrequency (%)
1134 1
< 0.1%
1101 1
< 0.1%
1049 1
< 0.1%
991 1
< 0.1%
916 1
< 0.1%
888 1
< 0.1%
872 1
< 0.1%
862 1
< 0.1%
855 1
< 0.1%
850 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct154
Distinct (%)< 0.1%
Missing4755640
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean0.076153874
Minimum0
Maximum573
Zeros1059955
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:41.934122image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum573
Range573
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1434596
Coefficient of variation (CV)28.146428
Kurtosis10141.818
Mean0.076153874
Median Absolute Deviation (MAD)0
Skewness72.127661
Sum80985
Variance4.5944189
MonotonicityNot monotonic
2024-02-18T01:42:42.003631image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1059955
 
18.2%
15 158
 
< 0.1%
8 127
 
< 0.1%
10 125
 
< 0.1%
12 124
 
< 0.1%
6 121
 
< 0.1%
13 119
 
< 0.1%
7 119
 
< 0.1%
9 117
 
< 0.1%
5 116
 
< 0.1%
Other values (144) 2358
 
< 0.1%
(Missing) 4755640
81.7%
ValueCountFrequency (%)
0 1059955
18.2%
1 99
 
< 0.1%
2 110
 
< 0.1%
3 104
 
< 0.1%
4 106
 
< 0.1%
5 116
 
< 0.1%
6 121
 
< 0.1%
7 119
 
< 0.1%
8 127
 
< 0.1%
9 117
 
< 0.1%
ValueCountFrequency (%)
573 1
 
< 0.1%
440 1
 
< 0.1%
364 1
 
< 0.1%
256 1
 
< 0.1%
241 1
 
< 0.1%
237 1
 
< 0.1%
227 2
< 0.1%
221 3
< 0.1%
215 1
 
< 0.1%
214 1
 
< 0.1%

AIRLINE_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1067
Distinct (%)0.1%
Missing4755640
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean18.969547
Minimum0
Maximum1971
Zeros493417
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:42.078676image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q319
95-th percentile87
Maximum1971
Range1971
Interquartile range (IQR)19

Descriptive statistics

Standard deviation48.161642
Coefficient of variation (CV)2.5388926
Kurtosis134.85064
Mean18.969547
Median Absolute Deviation (MAD)2
Skewness8.5250098
Sum20172956
Variance2319.5438
MonotonicityNot monotonic
2024-02-18T01:42:42.148675image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 493417
 
8.5%
1 21319
 
0.4%
2 21211
 
0.4%
3 20656
 
0.4%
4 20184
 
0.3%
6 20107
 
0.3%
5 19772
 
0.3%
7 18646
 
0.3%
8 17494
 
0.3%
15 16582
 
0.3%
Other values (1057) 394051
 
6.8%
(Missing) 4755640
81.7%
ValueCountFrequency (%)
0 493417
8.5%
1 21319
 
0.4%
2 21211
 
0.4%
3 20656
 
0.4%
4 20184
 
0.3%
5 19772
 
0.3%
6 20107
 
0.3%
7 18646
 
0.3%
8 17494
 
0.3%
9 16368
 
0.3%
ValueCountFrequency (%)
1971 1
< 0.1%
1878 1
< 0.1%
1665 1
< 0.1%
1636 1
< 0.1%
1631 1
< 0.1%
1625 1
< 0.1%
1593 1
< 0.1%
1587 1
< 0.1%
1576 1
< 0.1%
1563 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct695
Distinct (%)0.1%
Missing4755640
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean23.472838
Minimum0
Maximum1331
Zeros506486
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:42.223184image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q329
95-th percentile107
Maximum1331
Range1331
Interquartile range (IQR)29

Descriptive statistics

Standard deviation43.197018
Coefficient of variation (CV)1.8402981
Kurtosis32.075947
Mean23.472838
Median Absolute Deviation (MAD)3
Skewness4.0166724
Sum24961931
Variance1865.9824
MonotonicityNot monotonic
2024-02-18T01:42:42.293691image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 506486
 
8.7%
15 14522
 
0.2%
16 13824
 
0.2%
17 12908
 
0.2%
18 12259
 
0.2%
19 11794
 
0.2%
14 11183
 
0.2%
20 11079
 
0.2%
13 10930
 
0.2%
11 10517
 
0.2%
Other values (685) 447937
 
7.7%
(Missing) 4755640
81.7%
ValueCountFrequency (%)
0 506486
8.7%
1 9575
 
0.2%
2 9388
 
0.2%
3 9012
 
0.2%
4 8854
 
0.2%
5 9038
 
0.2%
6 9501
 
0.2%
7 9629
 
0.2%
8 9912
 
0.2%
9 9897
 
0.2%
ValueCountFrequency (%)
1331 1
< 0.1%
1313 1
< 0.1%
1294 1
< 0.1%
1256 1
< 0.1%
1190 1
< 0.1%
1174 1
< 0.1%
1164 1
< 0.1%
1102 1
< 0.1%
1057 1
< 0.1%
1039 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct632
Distinct (%)0.1%
Missing4755640
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean2.9152899
Minimum0
Maximum1211
Zeros998723
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size44.4 MiB
2024-02-18T01:42:42.361693image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum1211
Range1211
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.433336
Coefficient of variation (CV)7.0090235
Kurtosis451.69916
Mean2.9152899
Median Absolute Deviation (MAD)0
Skewness16.308217
Sum3100233
Variance417.52121
MonotonicityNot monotonic
2024-02-18T01:42:42.433797image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 998723
 
17.2%
6 1649
 
< 0.1%
8 1580
 
< 0.1%
7 1537
 
< 0.1%
15 1498
 
< 0.1%
10 1498
 
< 0.1%
9 1487
 
< 0.1%
16 1460
 
< 0.1%
5 1415
 
< 0.1%
3 1412
 
< 0.1%
Other values (622) 51180
 
0.9%
(Missing) 4755640
81.7%
ValueCountFrequency (%)
0 998723
17.2%
1 1308
 
< 0.1%
2 1397
 
< 0.1%
3 1412
 
< 0.1%
4 1319
 
< 0.1%
5 1415
 
< 0.1%
6 1649
 
< 0.1%
7 1537
 
< 0.1%
8 1580
 
< 0.1%
9 1487
 
< 0.1%
ValueCountFrequency (%)
1211 1
< 0.1%
1152 1
< 0.1%
1120 1
< 0.1%
1118 1
< 0.1%
1116 1
< 0.1%
1068 1
< 0.1%
1052 1
< 0.1%
1039 1
< 0.1%
1035 1
< 0.1%
1021 1
< 0.1%

LATE_AIRCRAFT_DELAY_CAT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.4 MiB
0
5414697 
1
 
147751
2
 
128482
3
 
128149

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5819079
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5414697
93.1%
1 147751
 
2.5%
2 128482
 
2.2%
3 128149
 
2.2%

Length

2024-02-18T01:42:42.496033image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-18T01:42:42.551540image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5414697
93.1%
1 147751
 
2.5%
2 128482
 
2.2%
3 128149
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 5414697
93.1%
1 147751
 
2.5%
2 128482
 
2.2%
3 128149
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5819079
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5414697
93.1%
1 147751
 
2.5%
2 128482
 
2.2%
3 128149
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5819079
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5414697
93.1%
1 147751
 
2.5%
2 128482
 
2.2%
3 128149
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5819079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5414697
93.1%
1 147751
 
2.5%
2 128482
 
2.2%
3 128149
 
2.2%

Date
Date

Distinct365
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.4 MiB
Minimum2015-01-01 00:00:00
Maximum2015-12-31 00:00:00
2024-02-18T01:42:42.607045image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:42:42.677138image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-02-18T01:41:57.675565image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:27.015604image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:33.983022image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:40.928037image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:47.934963image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:55.167393image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:02.328333image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:09.523998image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:16.967978image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:24.274033image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:31.521897image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:38.887496image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:45.711848image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:48.650148image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:51.575911image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:54.688598image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:57.859321image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:27.541819image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:34.490295image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:41.449777image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:48.491078image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:55.735656image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:02.904197image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:10.104563image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:17.556070image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:24.826646image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:32.100544image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:39.463775image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:45.894867image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:48.830164image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:51.757928image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:54.875617image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:58.042832image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:28.068323image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:35.013435image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:41.956579image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:49.043522image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:56.304941image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:03.481618image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:10.687232image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:18.138401image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:25.382738image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:32.679751image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:40.036421image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:46.077446image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:49.012682image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:51.946945image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:55.063633image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:58.228845image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:28.600094image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:35.549797image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:42.475533image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:49.591741image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:56.886220image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:04.056701image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:11.277735image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:18.733129image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:25.933571image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:33.260150image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:40.613011image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:46.265465image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2024-02-18T01:41:55.630189image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:58.773036image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:30.299274image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:37.238459image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:44.140025image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:51.343681image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:58.553240image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:05.726287image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:13.060447image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:20.465683image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:27.687126image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:34.978766image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:42.298267image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:46.813391image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:49.745726image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:52.697332image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:55.809675image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:58.957299image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:30.863856image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2024-02-18T01:40:44.797308image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2024-02-18T01:40:59.114787image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:06.292739image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:13.651329image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:21.017860image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:28.279032image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:35.560902image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:42.867006image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:47.001664image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:49.933472image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:52.886066image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:55.998246image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:59.141811image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:31.404564image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:38.351437image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:45.332478image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:52.496113image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:59.690050image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:06.869067image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:14.259888image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:21.611672image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:28.824592image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:36.147415image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:43.439611image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:47.186681image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:50.121094image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:53.076082image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:56.189263image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:59.328825image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:31.967823image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:38.912755image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:45.889271image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:53.087049image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:00.247489image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:07.435337image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:14.841780image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:22.186289image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:29.411298image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:36.707606image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:44.003086image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:47.371697image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:50.305116image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:53.267107image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:56.377280image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:59.510549image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:32.532748image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:39.480277image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:46.454062image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:53.672665image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:00.794683image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:07.998512image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:15.421838image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:22.759243image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:29.991220image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:37.288183image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:44.558154image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:47.551716image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:50.484131image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:53.452812image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:56.562326image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:59.696657image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:32.712765image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:39.661295image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:46.643640image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:53.857214image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:00.982258image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:08.189273image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:15.614573image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:22.949265image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:30.181401image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:37.472918image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:44.745339image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:47.725736image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:50.667149image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:53.651833image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:56.752349image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:59.880684image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:32.893278image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:39.842391image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:46.823313image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:54.042348image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:01.171956image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:08.373801image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:15.803798image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:23.135281image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:30.369420image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:37.653946image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:44.936405image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:47.907317image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:50.830660image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:53.941854image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:56.934855image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:42:00.066331image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:33.079583image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:40.019410image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:47.006371image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:54.232873image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:01.363583image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:08.560007image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:15.995842image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:23.327797image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:30.561441image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:37.843206image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:45.130154image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:48.096098image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:51.013731image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:54.118531image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:57.122869image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:42:00.260027image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:33.270797image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:40.211428image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:47.196932image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:54.421891image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:01.563608image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:08.749026image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:16.188866image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:23.525812image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:30.754463image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:38.037235image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:45.332685image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:48.284115image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:51.205875image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:54.311631image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:57.305521image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:42:00.425536image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:33.453820image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:40.398941image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:47.378162image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:40:54.607403image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:01.752247image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:08.936044image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:16.379884image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:23.712331image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:30.940473image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:38.225264image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:45.523833image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:48.471130image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:51.389893image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:54.501582image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-02-18T01:41:57.492538image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2024-02-18T01:42:42.845308image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
MONTHDAYDAY_OF_WEEKFLIGHT_NUMBERDEPARTURE_DELAYTAXI_OUTELAPSED_TIMEAIR_TIMEDISTANCETAXI_INARRIVAL_DELAYAIR_SYSTEM_DELAYSECURITY_DELAYAIRLINE_DELAYLATE_AIRCRAFT_DELAYWEATHER_DELAYAIRLINEDIVERTEDCANCELLEDCANCELLATION_REASONLATE_AIRCRAFT_DELAY_CAT
MONTH1.0000.009-0.008-0.014-0.0310.002-0.0000.0010.0090.014-0.061-0.0290.0120.006-0.006-0.0280.0710.0120.0810.1990.035
DAY0.0091.0000.0010.002-0.004-0.0030.0020.0020.004-0.001-0.008-0.0150.0040.0070.0000.0080.0020.0050.0400.1450.011
DAY_OF_WEEK-0.0080.0011.0000.017-0.004-0.0180.0110.0140.017-0.000-0.018-0.0160.0060.033-0.020-0.0120.0110.0050.0360.0880.015
FLIGHT_NUMBER-0.0140.0020.0171.000-0.0590.064-0.285-0.305-0.321-0.0260.0150.001-0.018-0.0570.043-0.0050.4250.0050.0580.1920.017
DEPARTURE_DELAY-0.031-0.004-0.004-0.0591.0000.0310.0850.0850.092-0.0470.641-0.392-0.0080.3040.4790.1110.0150.0160.0270.0330.264
TAXI_OUT0.002-0.003-0.0180.0640.0311.0000.2240.1050.0920.0110.2500.487-0.003-0.132-0.2340.0860.0530.0120.0050.0550.022
ELAPSED_TIME-0.0000.0020.011-0.2850.0850.2241.0000.9830.9670.1930.0240.2700.008-0.005-0.1900.0180.1571.0001.0000.0000.016
AIR_TIME0.0010.0020.014-0.3050.0850.1050.9831.0000.9880.126-0.0340.1100.0100.051-0.128-0.0030.1601.0001.0000.0000.018
DISTANCE0.0090.0040.017-0.3210.0920.0920.9670.9881.0000.111-0.0650.0340.0110.078-0.099-0.0060.1710.0150.0330.0760.018
TAXI_IN0.014-0.001-0.000-0.026-0.0470.0110.1930.1260.1111.0000.0980.289-0.006-0.120-0.092-0.0090.0260.0201.0000.0000.011
ARRIVAL_DELAY-0.061-0.008-0.0180.0150.6410.2500.024-0.034-0.0650.0981.0000.018-0.0130.1900.3630.1350.0161.0001.0000.0000.267
AIR_SYSTEM_DELAY-0.029-0.015-0.0160.001-0.3920.4870.2700.1100.0340.2890.0181.000-0.009-0.362-0.355-0.0000.0261.0001.0000.0000.036
SECURITY_DELAY0.0120.0040.006-0.018-0.008-0.0030.0080.0100.011-0.006-0.013-0.0091.000-0.049-0.018-0.0130.0051.0001.0000.0000.005
AIRLINE_DELAY0.0060.0070.033-0.0570.304-0.132-0.0050.0510.078-0.1200.190-0.362-0.0491.000-0.213-0.2200.0181.0001.0000.0000.037
LATE_AIRCRAFT_DELAY-0.0060.000-0.0200.0430.479-0.234-0.190-0.128-0.099-0.0920.363-0.355-0.018-0.2131.000-0.0150.0181.0001.0000.0000.280
WEATHER_DELAY-0.0280.008-0.012-0.0050.1110.0860.018-0.003-0.006-0.0090.135-0.000-0.013-0.220-0.0151.0000.0131.0001.0000.0000.019
AIRLINE0.0710.0020.0110.4250.0150.0530.1570.1600.1710.0260.0160.0260.0050.0180.0180.0131.0000.0090.0840.2370.046
DIVERTED0.0120.0050.0050.0050.0160.0121.0001.0000.0150.0201.0001.0001.0001.0001.0001.0000.0091.0000.0061.0000.014
CANCELLED0.0810.0400.0360.0580.0270.0051.0001.0000.0331.0001.0001.0001.0001.0001.0001.0000.0840.0061.0001.0000.034
CANCELLATION_REASON0.1990.1450.0880.1920.0330.0550.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.2371.0001.0001.0001.000
LATE_AIRCRAFT_DELAY_CAT0.0350.0110.0150.0170.2640.0220.0160.0180.0180.0110.2670.0360.0050.0370.2800.0190.0460.0140.0341.0001.000

Missing values

2024-02-18T01:42:04.401197image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-18T01:42:12.776724image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-18T01:42:31.938492image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

YEARMONTHDAYDAY_OF_WEEKAIRLINEFLIGHT_NUMBERTAIL_NUMBERORIGIN_AIRPORTDESTINATION_AIRPORTSCHEDULED_DEPARTUREDEPARTURE_TIMEDEPARTURE_DELAYTAXI_OUTWHEELS_OFFSCHEDULED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEWHEELS_ONTAXI_INSCHEDULED_ARRIVALARRIVAL_TIMEARRIVAL_DELAYDIVERTEDCANCELLEDCANCELLATION_REASONAIR_SYSTEM_DELAYSECURITY_DELAYAIRLINE_DELAYLATE_AIRCRAFT_DELAYWEATHER_DELAYLATE_AIRCRAFT_DELAY_CATDate
02015114AS98N407ASANCSEA00:05 AM11:54 PM-11.021.000:15 AM02:05 AM194.0169.0144804:04 AM4.004:30 AM04:08 AM-22.000NaNNaNNaNNaNNaNNaN001/01/2015
12015114AA2336N3KUAALAXPBI00:10 AM00:02 AM-8.012.000:14 AM02:80 AM279.0263.0233007:37 AM4.007:50 AM07:41 AM-9.000NaNNaNNaNNaNNaNNaN001/01/2015
22015114US840N171USSFOCLT00:20 AM00:18 AM-2.016.000:34 AM02:86 AM293.0266.0229608:00 AM11.008:06 AM08:11 AM5.000NaNNaNNaNNaNNaNNaN001/01/2015
32015114AA258N3HYAALAXMIA00:20 AM00:15 AM-5.015.000:30 AM02:85 AM281.0258.0234207:48 AM8.008:05 AM07:56 AM-9.000NaNNaNNaNNaNNaNNaN001/01/2015
42015114AS135N527ASSEAANC00:25 AM00:24 AM-1.011.000:35 AM02:35 AM215.0199.0144802:54 AM5.003:20 AM02:59 AM-21.000NaNNaNNaNNaNNaNNaN001/01/2015
52015114DL806N3730BSFOMSP00:25 AM00:20 AM-5.018.000:38 AM02:17 AM230.0206.0158906:04 AM6.006:02 AM06:10 AM8.000NaNNaNNaNNaNNaNNaN001/01/2015
62015114NK612N635NKLASMSP00:25 AM00:19 AM-6.011.000:30 AM01:81 AM170.0154.0129905:04 AM5.005:26 AM05:09 AM-17.000NaNNaNNaNNaNNaNNaN001/01/2015
72015114US2013N584UWLAXCLT00:30 AM00:44 AM14.013.000:57 AM02:73 AM249.0228.0212507:45 AM8.008:03 AM07:53 AM-10.000NaNNaNNaNNaNNaNNaN001/01/2015
82015114AA1112N3LAAASFODFW00:30 AM00:19 AM-11.017.000:36 AM01:95 AM193.0173.0146405:29 AM3.005:45 AM05:32 AM-13.000NaNNaNNaNNaNNaNNaN001/01/2015
92015114DL1173N826DNLASATL00:30 AM00:33 AM3.012.000:45 AM02:21 AM203.0186.0174706:51 AM5.007:11 AM06:56 AM-15.000NaNNaNNaNNaNNaNNaN001/01/2015
YEARMONTHDAYDAY_OF_WEEKAIRLINEFLIGHT_NUMBERTAIL_NUMBERORIGIN_AIRPORTDESTINATION_AIRPORTSCHEDULED_DEPARTUREDEPARTURE_TIMEDEPARTURE_DELAYTAXI_OUTWHEELS_OFFSCHEDULED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEWHEELS_ONTAXI_INSCHEDULED_ARRIVALARRIVAL_TIMEARRIVAL_DELAYDIVERTEDCANCELLEDCANCELLATION_REASONAIR_SYSTEM_DELAYSECURITY_DELAYAIRLINE_DELAYLATE_AIRCRAFT_DELAYWEATHER_DELAYLATE_AIRCRAFT_DELAY_CATDate
5819069201512314B61248N948JBLASJFK11:59 PM02:38 AM159.034.003:12 AM02:82 AM282.0243.0224810:15 AM5.007:41 AM10:20 AM159.000NaN0.00.0159.00.00.0031/12/2015
5819070201512314B680N584JBRNOJFK11:59 PM11:59 PM0.012.000:11 AM03:06 AM285.0268.0241107:39 AM5.008:05 AM07:44 AM-21.000NaNNaNNaNNaNNaNNaN031/12/2015
5819071201512314B6802N589JBSLCMCO11:59 PM00:15 AM16.014.000:29 AM02:49 AM250.0211.0193106:00 AM25.006:08 AM06:25 AM17.000NaN1.00.016.00.00.0031/12/2015
5819072201512314B698N607JBDENJFK11:59 PM00:06 AM7.013.000:19 AM02:11 AM193.0173.0162605:12 AM7.005:30 AM05:19 AM-11.000NaNNaNNaNNaNNaNNaN031/12/2015
5819073201512314B666N655JBABQJFK11:59 PM00:15 AM16.09.000:24 AM02:27 AM214.0190.0182605:34 AM15.005:46 AM05:49 AM3.000NaNNaNNaNNaNNaNNaN031/12/2015
5819074201512314B6688N657JBLAXBOS11:59 PM11:55 PM-4.022.000:17 AM03:20 AM298.0272.0261107:49 AM4.008:19 AM07:53 AM-26.000NaNNaNNaNNaNNaNNaN031/12/2015
5819075201512314B6745N828JBJFKPSE11:59 PM11:55 PM-4.017.000:12 AM02:27 AM215.0195.0161704:27 AM3.004:46 AM04:30 AM-16.000NaNNaNNaNNaNNaNNaN031/12/2015
5819076201512314B61503N913JBJFKSJU11:59 PM11:50 PM-9.017.000:07 AM02:21 AM222.0197.0159804:24 AM8.004:40 AM04:32 AM-8.000NaNNaNNaNNaNNaNNaN031/12/2015
5819077201512314B6333N527JBMCOSJU11:59 PM11:53 PM-6.010.000:03 AM01:61 AM157.0144.0118903:27 AM3.003:40 AM03:30 AM-10.000NaNNaNNaNNaNNaNNaN031/12/2015
5819078201512314B6839N534JBJFKBQN11:59 PM00:14 AM15.014.000:28 AM02:21 AM208.0189.0157604:37 AM5.004:40 AM04:42 AM2.000NaNNaNNaNNaNNaNNaN031/12/2015